DE CANARY G vs P

Pre-processing of the data

ls_preprocessed <- preprocess_rna(path_rnaseq = 'rnaseq.RData', correct_batch = T, correct_gender = F)

Exploring data

Batch effect correction

print(ls_preprocessed$pbatch_bf)

print(ls_preprocessed$pgender_bf)

print(ls_preprocessed$pbatch_af)

print(ls_preprocessed$pgender_af)

DE analysis

DE_res <- DE_analysis(ls_preprocessed, 
           GeneBased=FALSE, 
           pDataBased=TRUE,
           NewCondition=FALSE,
           cond_nm='CANARY',
           two_levels=c('P','G'),
           reference = 'G',
           correct_gender=FALSE)

## Unlist done

## Labeling done

## Filtering done

## factor levels were dropped which had no samples

## Design done

## Warning: Column `gene`/`ENSEMBL` joining character vector and factor, coercing
## into character vector

## vsd symbols done

## using pre-existing size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 2089 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

## DESeq done

## using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
## 
## Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
## See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
## Reference: https://doi.org/10.1093/bioinformatics/bty895

## Warning: Column `gene`/`ENSEMBL` joining character vector and factor, coercing
## into character vector

## res symbols done

## list done

DE results

heatmap_200(DE_res$res_df, DE_res$vsd_mat_sym, DE_res$meta_data, DE_res$pData_rnaseq)

volcano_plot(DE_res$res_df, gene=NULL, p_title='CANARY: G vs P')

Pathway enrichment analysis fGSEA

CANARY Good prognosis (G) is the reference. When sample is P, pathways shown below are up- or down- regulated

fgsea_res <- fgsea_analysis(DE_res)

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.13% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.13% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.13% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.13% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.13% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.13% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.13% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.13% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.13% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

fgsea_plot(fgsea_res$res_hm, pathways_title='Hallmark', condition_name='CANARY: G vs P')

fgsea_plot(fgsea_res$res_c1, pathways_title='C1 positional genes', condition_name='CANARY: G vs P')

fgsea_plot(fgsea_res$res_c2, pathways_title='C2 curated genes', condition_name='CANARY: G vs P')

fgsea_plot(fgsea_res$res_c3, pathways_title='C3 regulatory target genes', condition_name='CANARY: G vs P')

fgsea_plot(fgsea_res$res_c4, pathways_title='C4 cancer', condition_name='CANARY: G vs P')

fgsea_plot(fgsea_res$res_c5, pathways_title='C5 GO genes', condition_name='CANARY: G vs P')

fgsea_plot(fgsea_res$res_c6, pathways_title='C6 oncogenic', condition_name='CANARY: G vs P')

fgsea_plot(fgsea_res$res_c7, pathways_title='C7 immunologic', condition_name='CANARY: G vs P')

fgsea_plot(fgsea_res$res_msg, pathways_title='All signatures', condition_name='CANARY: G vs P')